Converting the practical salinity and potential temperature from ECCOv4r4 model output.

First, add the required dependencies

using OceanRasterConversions, Rasters, Plots, Downloads

and download model output from ECCOv4r4. This data is the daily average 0.5 degree salinity and temperature model output. To reproduce this example, an Earthdata acount is needed to download the data.

Read the data into a RasterStack

Downloads.download("https://opendap.earthdata.nasa.gov/providers/POCLOUD/collections/ECCO%2520Ocean%2520Temperature%2520and%2520Salinity%2520-%2520Daily%2520Mean%25200.5%2520Degree%2520(Version%25204%2520Release%25204)/granules/OCEAN_TEMPERATURE_SALINITY_day_mean_2007-01-01_ECCO_V4r4_latlon_0p50deg.dap.nc4", "ECCO_data.nc")

stack = RasterStack("ECCO_data.nc")

RasterStack with dimensions: 
  Ti Sampled{Dates.DateTime} Dates.DateTime[2007-01-01T12:00:00] ForwardOrdered Explicit Intervals,
  Z Sampled{Float32} Float32[-5.0, -15.0, …, -5461.25, -5906.25] ReverseOrdered Explicit Intervals,
  Y Mapped{Float32} Float32[-89.75, -89.25, …, 89.25, 89.75] ForwardOrdered Explicit Intervals crs: EPSG mappedcrs: EPSG,
  X Mapped{Float32} Float32[-179.75, -179.25, …, 179.25, 179.75] ForwardOrdered Explicit Intervals crs: EPSG mappedcrs: EPSG,
  Dim{:nv} Sampled{Float32} Float32[0.0, 1.0] ForwardOrdered Regular Points
and 6 layers:
  :SALT           Union{Missing, Float32} dims: X, Y, Z, Ti (720×360×50×1)
  :THETA          Union{Missing, Float32} dims: X, Y, Z, Ti (720×360×50×1)
  :Z_bnds         Union{Missing, Float32} dims: Dim{:nv}, Z (2×50)
  :latitude_bnds  Union{Missing, Float32} dims: Dim{:nv}, Y (2×360)
  :time_bnds      Union{Missing, Dates.DateTime} dims: Dim{:nv}, Ti (2×1)
  :longitude_bnds Union{Missing, Float32} dims: Dim{:nv}, X (2×720)

with metadata Metadata{Rasters.NCDfile} of Dict{String, Any} with 64 entries:
  "product_time_coverage_end"   => "2018-01-01T00:00:00"
  "acknowledgement"             => "This research was carried out by the Jet Pr…
  "cdm_data_type"               => "Grid"
  "time_coverage_resolution"    => "P1D"
  "time_coverage_duration"      => "P1D"
  "history"                     => "Inaugural release of an ECCO Central Estima…
  "author"                      => "Ian Fenty and Ou Wang"
  "product_version"             => "Version 4, Release 4"
  "geospatial_bounds_crs"       => "EPSG:4326"
  "publisher_type"              => "institution"
  "geospatial_lat_units"        => "degrees_north"
  "geospatial_lon_max"          => 180.0
  "geospatial_lon_units"        => "degrees_east"
  "date_metadata_modified"      => "2021-03-15T23:18:02"
  "geospatial_vertical_max"     => 0.0
  "product_time_coverage_start" => "1992-01-01T12:00:00"
  "publisher_url"               => "https://podaac.jpl.nasa.gov"
  "comment"                     => "Fields provided on a regular lat-lon grid. …
  "naming_authority"            => "gov.nasa.jpl"
  ⋮                             => ⋮from file:
ECCO_data.nc

Thanks to Rasters.jl we now have the dimensions of the data, the variables saved as layers and all the metadata in one data structure. From the metadata we can get a summary of the data which tells us more about the data

metadata(stack)["summary"]

"This dataset provides daily-averaged ocean potential temperature and salinity interpolated to a regular 0.5-degree grid from the ECCO Version 4 Release 4 (V4r4) ocean and sea-ice state estimate. Estimating the Circulation and Climate of the Ocean (ECCO) state estimates a" ⋯ 914 bytes ⋯ "dable bathythermographs (XBTs) from several programs [e.g., WOCE, GO-SHIP, Argo, and others] and platforms [e.g., research vessels, gliders, moorings, ice-tethered profilers, and instrumented pinnipeds]. V4r4 covers the period 1992-01-01T12:00:00 to 2018-01-01T00:00:00."

This tells us that the temperature variable is potential temperature and the salt variabile is practical salinity (for more information about this data see the user guide).

Converting variables and plotting

To calculate seawater density using TEOS-10, we require absolute salinity and conservative temperature. This can be done by extracting the data and using GibbsSeaWater.jl or with this package,

converted_stack = convert_ocean_vars(stack, (Sₚ = :SALT, θ = :THETA))

RasterStack with dimensions: 
  X Mapped{Float32} Float32[-179.75, -179.25, …, 179.25, 179.75] ForwardOrdered Explicit Intervals crs: EPSG mappedcrs: EPSG,
  Y Mapped{Float32} Float32[-89.75, -89.25, …, 89.25, 89.75] ForwardOrdered Explicit Intervals crs: EPSG mappedcrs: EPSG,
  Z Sampled{Float32} Float32[-5.0, -15.0, …, -5461.25, -5906.25] ReverseOrdered Explicit Intervals,
  Ti Sampled{Dates.DateTime} Dates.DateTime[2007-01-01T12:00:00] ForwardOrdered Explicit Intervals
and 4 layers:
  :p  Union{Missing, Float32} dims: X, Y, Z, Ti (720×360×50×1)
  :Sₐ Union{Missing, Float32} dims: X, Y, Z, Ti (720×360×50×1)
  :Θ  Union{Missing, Float32} dims: X, Y, Z, Ti (720×360×50×1)
  :ρ  Union{Missing, Float32} dims: X, Y, Z, Ti (720×360×50×1)

Note that this is a new RasterStack, so the metadata from the original RasterStack is not attached. As we have a returned RasterStack and plotting recipes have been written, we can then take slices of the data to look at depth-latitude plots of the returned variables (note by defaul the in-situ density ρ is computed and returned)

lon = 180
var_plots = plot(; layout = (4, 1), size = (900, 1000))
for (i, key) ∈ enumerate(keys(converted_stack))
    contourf!(var_plots[i], converted_stack[key][X(Near(lon))])
end
var_plots

As this is a RasterStack all methods exported by Rasters.jl will work. See the documentation for Rasters.jl for more information.

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